2,373 research outputs found
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
Resource Allocation for Downlink Multi-Cell OFDMA Cognitive Radio Network Using Hungarian Method
This paper considers the problem of resource allocation for downlink part of an OFDM-based multi-cell cognitive radio network which consists of multiple secondary transmitters and receivers communicating simultaneously in the presence of multiple primary users. We present a new framework to maximize the total data throughput of secondary users by means of subchannel assignment, while ensuring interference leakage to PUs is below a threshold. In this framework, we first formulate the resource allocation problem as a nonlinear and non-convex optimization problem. Then we represent the problem as a maximum weighted matching in a bipartite graph and propose an iterative algorithm based on Hungarian method to solve it. The present contribution develops an efficient subchannel allocation algorithm that assigns subchannels to the secondary users without the perfect knowledge of fading channel gain between cognitive radio transmitter and primary receivers. The performance of the proposed subcarrier allocation algorithm is compared with a blind subchannel allocation as well as another scheme with the perfect knowledge of channel-state information. Simulation results reveal that a significant performance advantage can still be realized, even if the optimization at the secondary network is based on imperfect network information
Interference-Aware Downlink Resource Management for OFDMA Femtocell Networks
Femtocell is an economical solution to provide high speed indoor communication instead of the conventional macro-cellular networks. Especially, OFDMA femtocell is considered in the next generation cellular network such as 3GPP LTE and mobile WiMAX system. Although the femtocell has great advantages to accommodate indoor users, interference management problem is a critical issue to operate femtocell network. Existing OFDMA resource management algorithms only consider optimizing system-centric metric, and cannot manage the co-channel interference. Moreover, it is hard to cooperate with other femtocells to control the interference, since the self-configurable characteristics of femtocell. This paper proposes a novel interference-aware resource allocation algorithm for OFDMA femtocell networks. The proposed algorithm allocates resources according to a new objective function which reflects the effect of interference, and the heuristic algorithm is also introduced to reduce the complexity of the original problem. The Monte-Carlo simulation is performed to evaluate the performance of the proposed algorithm compared to the existing solutions
SYNCHRONIZATION AND RESOURCE ALLOCATION IN DOWNLINK OFDM SYSTEMS
The next generation (4G) wireless systems are expected to provide
universal personal and multimedia communications with seamless connection
and very high rate transmissions and without regard to the usersâ mobility and
location. OFDM technique is recognized as one of the leading candidates to
provide the wireless signalling for 4G systems. The major challenges in
downlink multiuser OFDM based 4G systems include the wireless channel, the
synchronization and radio resource management. Thus algorithms are required
to achieve accurate timing and frequency offset estimation and the efficient
utilization of radio resources such as subcarrier, bit and power allocation.
The objectives of the thesis are of two fields. Firstly, we presented the
frequency offset estimation algorithms for OFDM systems. Building our work
upon the classic single user OFDM architecture, we proposed two FFT-based
frequency offset estimation algorithms with low computational complexity.
The computer simulation results and comparisons show that the proposed
algorithms provide smaller error variance than previous well-known algorithm.
Secondly, we presented the resource allocation algorithms for OFDM
systems. Building our work upon the downlink multiuser OFDM architecture,
we aimed to minimize the total transmit power by exploiting the system
diversity through the management of subcarrier allocation, adaptive
modulation and power allocation. Particularly, we focused on the dynamic
resource allocation algorithms for multiuser OFDM system and multiuser
MIMO-OFDM system. For the multiuser OFDM system, we proposed a lowiv
complexity channel gain difference based subcarrier allocation algorithm. For
the multiuser MIMO-OFDM system, we proposed a unit-power based
subcarrier allocation algorithm. These proposed algorithms are all combined
with the optimal bit allocation algorithm to achieve the minimal total transmit
power. The numerical results and comparisons with various conventional nonadaptive
and adaptive algorithmic approaches are provided to show that the
proposed resource allocation algorithms improve the system efficiencies and
performance given that the Quality of Service (QoS) for each user is
guaranteed.
The simulation work of this project is based on hand written codes in the
platform of the MATLAB R2007b
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